Comparing EKF and SPKF Algorithms for Simultaneous Localization and Mapping (SLAM)

Abstract

Simultaneous Localization and Mapping (SLAM) is the problem in which a sensor-enabled mobile robot incrementally builds a map for an unknown environment, while localizing itself within this map. A problem with detection of correct path of moving objects is the received noisy data. Therefore, it is possible that the information is incorrectly detected. The Kalman Filter's linearized error propagation can result in big errors and instability in the SLAM problem. One approach to reduce this situation is using of iteration in Extended Kalman Filter (EKF) and Sigma Point Kalman Filter (SPKF). We will show that the recapitulate versions of kalman filters can improve the estimation accuracy and robustness of these filters beside of linear error propagation. Simulation results are presented to validate this improvement of state estimate convergence through repetitive linearization of the nonlinear model in EKF and SPKF for SLAM algorithms. Results of this evaluation are introduced by computer simulations and verified by offline implementation of the SLAM algorithm on mobile robot in MRL Robotic Lab.